Parking lot monitor using visual data is not new, and other works have already tackled it in the literature. In [1]}, [2]}, the same authors of the CNRPark-EXT dataset presented a deep learning-based system for parking lot occupancy detection that can run in a Raspberry Pi directly on board a smart camera. In [3]}, the authors directly dealt with the issues deriving from the adoption of a multi-camera system. In particular, they applied a homography to project the detected vehicles from the plane of each camera to a common plane, where they performed a perspective correction to correct matching between the vehicle detections and the parking spots. Also, the authors in [4]} proposed a multi-camera system to classify parking spaces as vacant or occupied. In this solution, the acquired images are processed onboard Raspberry Pi devices. The extracted information about the status of parking spaces is then transmitted to a central server, which evaluates the parking spaces in the overlapping areas. Their algorithm is based on the histogram of oriented gradients (HOG)[5]} feature descriptor and svm classifiers. Since the HOG feature descriptor cannot adequately describe rotated vehicles, the authors have provided a descriptor with additional information about rotation to increase the system accuracy. However, these solutions rely on prior knowledge of the monitored scene, like the position of the parking spaces or some geometric information about the scene. In essence, a preliminary annotation of the new areas and a new training phase of the algorithm are often mandatory operations.
As a consequence, these techniques are not very flexible. On the other hand, we propose a simple yet effective solution that does not need extra information about the monitored scene. The smart cameras can automatically localize and count the vehicles present in their field of view, propagating the single results to the other devices through messages. A decentralized technique, again running directly onboard the devices, is instead in charge of analyzing and merging these results, exploiting the captured images geometry, and automatically outputs the number of cars present in the entire parking area.
